In the face of the modern-day revolution in electronic communications, hard-copy communication media, such as hard-copy mail and documents, are alive and well. In fact, a substantial segment of the communication field relies, to this day, on the use of hard-copy documents which bear a human signature, typically that of the originator or sender of the document.
One example of such documents is the personal check, written against a party's bank account, and signed by that party. Another example is affidavits, the class of forms or other documents which are required to be signed. Sometimes, affidavits must even be signed under oath, for instance signed while a notary public witnesses the signature. A common category of affidavit-type forms is Internal Revenue Service tax forms.
Many types of hard-copy documents require some sort of processing. Typically, a sender generates the document to provide a recipient with some sort of information which the recipient requires. In the case of personal checks, for instance, the sender, who makes out the check, wishes to transfer funds from an account to the recipient.
Processing by the recipient generally involves extracting information from the document and taking suitable action based on the content of the extracted information. For instance the recipient of a check, a creditor of the sender, extracts the dollar sum from the check and identifies the sender, so that the recipient can credit the sender for the payment.
Processing hard-copy documents can be a complex and labor-intensive task, depending on the type of forms and the sort of information the documents bear. Various mechanisms for handling documents, and scanning them to extract information for them, have been developed. Because of the sheer volume of checks and other such documents, such automated handling and scanning is a virtual necessity. For instance, banks use automatic handlers and scanners to extract information from checks. To accommodate these systems, checks are printed with machine-readable inks using standardized, machine-recognizeable character sets.
However, one particular problem, which automatic systems have not handled in a satisfactory manner, is that of verifying signatures. In the case of checks, for instance, a bank will typically have on file a sample signature of an account holder. Any check drawn against the account holder's account should bear the account holder's signature. Ideally, for each check, the bank should verify the signature on the check against the sample signature.
Validating a signature, however, is not an easy task, since an individual's handwriting inevitably has certain variations from one sample to another. A human clerk, visually comparing the signatures, might well be able to both (i) recognize an authentic signature even though it does not identically match a sample signature on record, and (ii) tell the difference between an authentic account holder's signature and someone else's signature. An automatic system, on the other hand, would require sophisticated artificial intelligence and/or pattern-recognition technology to even make the attempt.
As a practical matter, institutions handling signed hard-copy documents have sometimes avoided the time and manpower costs by simply refraining from routinely comparing signatures. This failure to verify a signature raises the possibility that, for instance, a bank might honor a fraudulent check with a non-matching signature, with no one being the wiser until the account holder notices the fraudulent debit from his or her account.
Therefore, there is a need for a system and method for verifying signatures which is effective to recognize false signatures, while being efficient enough to avoid the time and manpower costs required for human signature verification.